development of an experimental research plan should not include a

Experimental design: Guide, steps, examples

Last updated

27 April 2023

Reviewed by

Miroslav Damyanov

Experimental research design is a scientific framework that allows you to manipulate one or more variables while controlling the test environment. 

When testing a theory or new product, it can be helpful to have a certain level of control and manipulate variables to discover different outcomes. You can use these experiments to determine cause and effect or study variable associations. 

This guide explores the types of experimental design, the steps in designing an experiment, and the advantages and limitations of experimental design. 

Make research less tedious

Dovetail streamlines research to help you uncover and share actionable insights

• What is experimental research design?

You can determine the relationship between each of the variables by: 

Manipulating one or more independent variables (i.e., stimuli or treatments)

Applying the changes to one or more dependent variables (i.e., test groups or outcomes)

With the ability to analyze the relationship between variables and using measurable data, you can increase the accuracy of the result. 

What is a good experimental design?

A good experimental design requires: 

Significant planning to ensure control over the testing environment

Sound experimental treatments

Properly assigning subjects to treatment groups

Without proper planning, unexpected external variables can alter an experiment's outcome. 

To meet your research goals, your experimental design should include these characteristics:

Provide unbiased estimates of inputs and associated uncertainties

Enable the researcher to detect differences caused by independent variables

Include a plan for analysis and reporting of the results

Provide easily interpretable results with specific conclusions

What's the difference between experimental and quasi-experimental design?

The major difference between experimental and quasi-experimental design is the random assignment of subjects to groups. 

A true experiment relies on certain controls. Typically, the researcher designs the treatment and randomly assigns subjects to control and treatment groups. 

However, these conditions are unethical or impossible to achieve in some situations.

When it's unethical or impractical to assign participants randomly, that’s when a quasi-experimental design comes in. 

This design allows researchers to conduct a similar experiment by assigning subjects to groups based on non-random criteria. 

Another type of quasi-experimental design might occur when the researcher doesn't have control over the treatment but studies pre-existing groups after they receive different treatments.

When can a researcher conduct experimental research?

Various settings and professions can use experimental research to gather information and observe behavior in controlled settings. 

Basically, a researcher can conduct experimental research any time they want to test a theory with variable and dependent controls. 

Experimental research is an option when the project includes an independent variable and a desire to understand the relationship between cause and effect. 

• The importance of experimental research design

Experimental research enables researchers to conduct studies that provide specific, definitive answers to questions and hypotheses. 

Researchers can test Independent variables in controlled settings to:

Test the effectiveness of a new medication

Design better products for consumers

Answer questions about human health and behavior

Developing a quality research plan means a researcher can accurately answer vital research questions with minimal error. As a result, definitive conclusions can influence the future of the independent variable. 

Types of experimental research designs

There are three main types of experimental research design. The research type you use will depend on the criteria of your experiment, your research budget, and environmental limitations. 

Pre-experimental research design

A pre-experimental research study is a basic observational study that monitors independent variables’ effects. 

During research, you observe one or more groups after applying a treatment to test whether the treatment causes any change. 

The three subtypes of pre-experimental research design are:

One-shot case study research design

This research method introduces a single test group to a single stimulus to study the results at the end of the application. 

After researchers presume the stimulus or treatment has caused changes, they gather results to determine how it affects the test subjects. 

One-group pretest-posttest design

This method uses a single test group but includes a pretest study as a benchmark. The researcher applies a test before and after the group’s exposure to a specific stimulus. 

Static group comparison design

This method includes two or more groups, enabling the researcher to use one group as a control. They apply a stimulus to one group and leave the other group static. 

A posttest study compares the results among groups. 

True experimental research design

A true experiment is the most common research method. It involves statistical analysis to prove or disprove a specific hypothesis . 

Under completely experimental conditions, researchers expose participants in two or more randomized groups to different stimuli. 

Random selection removes any potential for bias, providing more reliable results. 

These are the three main sub-groups of true experimental research design:

Posttest-only control group design

This structure requires the researcher to divide participants into two random groups. One group receives no stimuli and acts as a control while the other group experiences stimuli.

Researchers perform a test at the end of the experiment to observe the stimuli exposure results.

Pretest-posttest control group design

This test also requires two groups. It includes a pretest as a benchmark before introducing the stimulus. 

The pretest introduces multiple ways to test subjects. For instance, if the control group also experiences a change, it reveals that taking the test twice changes the results.

Solomon four-group design

This structure divides subjects into two groups, with two as control groups. Researchers assign the first control group a posttest only and the second control group a pretest and a posttest. 

The two variable groups mirror the control groups, but researchers expose them to stimuli. The ability to differentiate between groups in multiple ways provides researchers with more testing approaches for data-based conclusions. 

Quasi-experimental research design

Although closely related to a true experiment, quasi-experimental research design differs in approach and scope. 

Quasi-experimental research design doesn’t have randomly selected participants. Researchers typically divide the groups in this research by pre-existing differences. 

Quasi-experimental research is more common in educational studies, nursing, or other research projects where it's not ethical or practical to use randomized subject groups.

• 5 steps for designing an experiment

Experimental research requires a clearly defined plan to outline the research parameters and expected goals. 

Here are five key steps in designing a successful experiment:

Step 1: Define variables and their relationship

Your experiment should begin with a question: What are you hoping to learn through your experiment? 

The relationship between variables in your study will determine your answer.

Define the independent variable (the intended stimuli) and the dependent variable (the expected effect of the stimuli). After identifying these groups, consider how you might control them in your experiment. 

Could natural variations affect your research? If so, your experiment should include a pretest and posttest. 

Step 2: Develop a specific, testable hypothesis

With a firm understanding of the system you intend to study, you can write a specific, testable hypothesis. 

What is the expected outcome of your study? 

Develop a prediction about how the independent variable will affect the dependent variable. 

How will the stimuli in your experiment affect your test subjects? 

Your hypothesis should provide a prediction of the answer to your research question . 

Step 3: Design experimental treatments to manipulate your independent variable

Depending on your experiment, your variable may be a fixed stimulus (like a medical treatment) or a variable stimulus (like a period during which an activity occurs). 

Determine which type of stimulus meets your experiment’s needs and how widely or finely to vary your stimuli. 

Step 4: Assign subjects to groups

When you have a clear idea of how to carry out your experiment, you can determine how to assemble test groups for an accurate study. 

When choosing your study groups, consider: 

The size of your experiment

Whether you can select groups randomly

Your target audience for the outcome of the study

You should be able to create groups with an equal number of subjects and include subjects that match your target audience. Remember, you should assign one group as a control and use one or more groups to study the effects of variables. 

Step 5: Plan how to measure your dependent variable

This step determines how you'll collect data to determine the study's outcome. You should seek reliable and valid measurements that minimize research bias or error. 

You can measure some data with scientific tools, while you’ll need to operationalize other forms to turn them into measurable observations.

• Advantages of experimental research

Experimental research is an integral part of our world. It allows researchers to conduct experiments that answer specific questions. 

While researchers use many methods to conduct different experiments, experimental research offers these distinct benefits:

Researchers can determine cause and effect by manipulating variables.

It gives researchers a high level of control.

Researchers can test multiple variables within a single experiment.

All industries and fields of knowledge can use it. 

Researchers can duplicate results to promote the validity of the study .

Replicating natural settings rapidly means immediate research.

Researchers can combine it with other research methods.

It provides specific conclusions about the validity of a product, theory, or idea.

• Disadvantages (or limitations) of experimental research

Unfortunately, no research type yields ideal conditions or perfect results. 

While experimental research might be the right choice for some studies, certain conditions could render experiments useless or even dangerous. 

Before conducting experimental research, consider these disadvantages and limitations:

Required professional qualification

Only competent professionals with an academic degree and specific training are qualified to conduct rigorous experimental research. This ensures results are unbiased and valid. 

Limited scope

Experimental research may not capture the complexity of some phenomena, such as social interactions or cultural norms. These are difficult to control in a laboratory setting.

Resource-intensive

Experimental research can be expensive, time-consuming, and require significant resources, such as specialized equipment or trained personnel.

Limited generalizability

The controlled nature means the research findings may not fully apply to real-world situations or people outside the experimental setting.

Practical or ethical concerns

Some experiments may involve manipulating variables that could harm participants or violate ethical guidelines . 

Researchers must ensure their experiments do not cause harm or discomfort to participants. 

Sometimes, recruiting a sample of people to randomly assign may be difficult. 

• Experimental research design example

Experiments across all industries and research realms provide scientists, developers, and other researchers with definitive answers. These experiments can solve problems, create inventions, and heal illnesses. 

Product design testing is an excellent example of experimental research. 

A company in the product development phase creates multiple prototypes for testing. With a randomized selection, researchers introduce each test group to a different prototype. 

When groups experience different product designs , the company can assess which option most appeals to potential customers. 

Experimental research design provides researchers with a controlled environment to conduct experiments that evaluate cause and effect. 

Using the five steps to develop a research plan ensures you anticipate and eliminate external variables while answering life’s crucial questions.

Get started today

Go from raw data to valuable insights with a flexible research platform

Editor’s picks

Last updated: 21 December 2023

Last updated: 16 December 2023

Last updated: 6 October 2023

Last updated: 25 November 2023

Last updated: 12 May 2023

Last updated: 15 February 2024

Last updated: 11 March 2024

Last updated: 12 December 2023

Last updated: 18 May 2023

Last updated: 6 March 2024

Last updated: 10 April 2023

Last updated: 20 December 2023

Latest articles

Related topics.

• Types of experimental

Log in or sign up

Get started for free

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, automatically generate references for free.

• Knowledge Base
• Methodology

Research Design | Step-by-Step Guide with Examples

Published on 5 May 2022 by Shona McCombes . Revised on 20 March 2023.

A research design is a strategy for answering your research question  using empirical data. Creating a research design means making decisions about:

• Your overall aims and approach
• The type of research design you’ll use
• Your sampling methods or criteria for selecting subjects
• Your data collection methods
• The procedures you’ll follow to collect data
• Your data analysis methods

A well-planned research design helps ensure that your methods match your research aims and that you use the right kind of analysis for your data.

Table of contents

Step 1: consider your aims and approach, step 2: choose a type of research design, step 3: identify your population and sampling method, step 4: choose your data collection methods, step 5: plan your data collection procedures, step 6: decide on your data analysis strategies, frequently asked questions.

• Introduction

Before you can start designing your research, you should already have a clear idea of the research question you want to investigate.

There are many different ways you could go about answering this question. Your research design choices should be driven by your aims and priorities – start by thinking carefully about what you want to achieve.

The first choice you need to make is whether you’ll take a qualitative or quantitative approach.

Qualitative research designs tend to be more flexible and inductive , allowing you to adjust your approach based on what you find throughout the research process.

Quantitative research designs tend to be more fixed and deductive , with variables and hypotheses clearly defined in advance of data collection.

It’s also possible to use a mixed methods design that integrates aspects of both approaches. By combining qualitative and quantitative insights, you can gain a more complete picture of the problem you’re studying and strengthen the credibility of your conclusions.

Practical and ethical considerations when designing research

As well as scientific considerations, you need to think practically when designing your research. If your research involves people or animals, you also need to consider research ethics .

• How much time do you have to collect data and write up the research?
• Will you be able to gain access to the data you need (e.g., by travelling to a specific location or contacting specific people)?
• Do you have the necessary research skills (e.g., statistical analysis or interview techniques)?
• Will you need ethical approval ?

At each stage of the research design process, make sure that your choices are practically feasible.

Prevent plagiarism, run a free check.

Within both qualitative and quantitative approaches, there are several types of research design to choose from. Each type provides a framework for the overall shape of your research.

Types of quantitative research designs

Quantitative designs can be split into four main types. Experimental and   quasi-experimental designs allow you to test cause-and-effect relationships, while descriptive and correlational designs allow you to measure variables and describe relationships between them.

With descriptive and correlational designs, you can get a clear picture of characteristics, trends, and relationships as they exist in the real world. However, you can’t draw conclusions about cause and effect (because correlation doesn’t imply causation ).

Experiments are the strongest way to test cause-and-effect relationships without the risk of other variables influencing the results. However, their controlled conditions may not always reflect how things work in the real world. They’re often also more difficult and expensive to implement.

Types of qualitative research designs

Qualitative designs are less strictly defined. This approach is about gaining a rich, detailed understanding of a specific context or phenomenon, and you can often be more creative and flexible in designing your research.

The table below shows some common types of qualitative design. They often have similar approaches in terms of data collection, but focus on different aspects when analysing the data.

Your research design should clearly define who or what your research will focus on, and how you’ll go about choosing your participants or subjects.

In research, a population is the entire group that you want to draw conclusions about, while a sample is the smaller group of individuals you’ll actually collect data from.

Defining the population

A population can be made up of anything you want to study – plants, animals, organisations, texts, countries, etc. In the social sciences, it most often refers to a group of people.

For example, will you focus on people from a specific demographic, region, or background? Are you interested in people with a certain job or medical condition, or users of a particular product?

The more precisely you define your population, the easier it will be to gather a representative sample.

Sampling methods

Even with a narrowly defined population, it’s rarely possible to collect data from every individual. Instead, you’ll collect data from a sample.

To select a sample, there are two main approaches: probability sampling and non-probability sampling . The sampling method you use affects how confidently you can generalise your results to the population as a whole.

Probability sampling is the most statistically valid option, but it’s often difficult to achieve unless you’re dealing with a very small and accessible population.

For practical reasons, many studies use non-probability sampling, but it’s important to be aware of the limitations and carefully consider potential biases. You should always make an effort to gather a sample that’s as representative as possible of the population.

Case selection in qualitative research

In some types of qualitative designs, sampling may not be relevant.

For example, in an ethnography or a case study, your aim is to deeply understand a specific context, not to generalise to a population. Instead of sampling, you may simply aim to collect as much data as possible about the context you are studying.

In these types of design, you still have to carefully consider your choice of case or community. You should have a clear rationale for why this particular case is suitable for answering your research question.

For example, you might choose a case study that reveals an unusual or neglected aspect of your research problem, or you might choose several very similar or very different cases in order to compare them.

Data collection methods are ways of directly measuring variables and gathering information. They allow you to gain first-hand knowledge and original insights into your research problem.

You can choose just one data collection method, or use several methods in the same study.

Survey methods

Surveys allow you to collect data about opinions, behaviours, experiences, and characteristics by asking people directly. There are two main survey methods to choose from: questionnaires and interviews.

Observation methods

Observations allow you to collect data unobtrusively, observing characteristics, behaviours, or social interactions without relying on self-reporting.

Observations may be conducted in real time, taking notes as you observe, or you might make audiovisual recordings for later analysis. They can be qualitative or quantitative.

Other methods of data collection

There are many other ways you might collect data depending on your field and topic.

If you’re not sure which methods will work best for your research design, try reading some papers in your field to see what data collection methods they used.

Secondary data

If you don’t have the time or resources to collect data from the population you’re interested in, you can also choose to use secondary data that other researchers already collected – for example, datasets from government surveys or previous studies on your topic.

With this raw data, you can do your own analysis to answer new research questions that weren’t addressed by the original study.

Using secondary data can expand the scope of your research, as you may be able to access much larger and more varied samples than you could collect yourself.

However, it also means you don’t have any control over which variables to measure or how to measure them, so the conclusions you can draw may be limited.

As well as deciding on your methods, you need to plan exactly how you’ll use these methods to collect data that’s consistent, accurate, and unbiased.

Planning systematic procedures is especially important in quantitative research, where you need to precisely define your variables and ensure your measurements are reliable and valid.

Operationalisation

Some variables, like height or age, are easily measured. But often you’ll be dealing with more abstract concepts, like satisfaction, anxiety, or competence. Operationalisation means turning these fuzzy ideas into measurable indicators.

If you’re using observations , which events or actions will you count?

If you’re using surveys , which questions will you ask and what range of responses will be offered?

You may also choose to use or adapt existing materials designed to measure the concept you’re interested in – for example, questionnaires or inventories whose reliability and validity has already been established.

Reliability and validity

Reliability means your results can be consistently reproduced , while validity means that you’re actually measuring the concept you’re interested in.

For valid and reliable results, your measurement materials should be thoroughly researched and carefully designed. Plan your procedures to make sure you carry out the same steps in the same way for each participant.

If you’re developing a new questionnaire or other instrument to measure a specific concept, running a pilot study allows you to check its validity and reliability in advance.

Sampling procedures

As well as choosing an appropriate sampling method, you need a concrete plan for how you’ll actually contact and recruit your selected sample.

That means making decisions about things like:

• How many participants do you need for an adequate sample size?
• What inclusion and exclusion criteria will you use to identify eligible participants?
• How will you contact your sample – by mail, online, by phone, or in person?

If you’re using a probability sampling method, it’s important that everyone who is randomly selected actually participates in the study. How will you ensure a high response rate?

If you’re using a non-probability method, how will you avoid bias and ensure a representative sample?

Data management

It’s also important to create a data management plan for organising and storing your data.

Will you need to transcribe interviews or perform data entry for observations? You should anonymise and safeguard any sensitive data, and make sure it’s backed up regularly.

Keeping your data well organised will save time when it comes to analysing them. It can also help other researchers validate and add to your findings.

On their own, raw data can’t answer your research question. The last step of designing your research is planning how you’ll analyse the data.

Quantitative data analysis

In quantitative research, you’ll most likely use some form of statistical analysis . With statistics, you can summarise your sample data, make estimates, and test hypotheses.

Using descriptive statistics , you can summarise your sample data in terms of:

• The distribution of the data (e.g., the frequency of each score on a test)
• The central tendency of the data (e.g., the mean to describe the average score)
• The variability of the data (e.g., the standard deviation to describe how spread out the scores are)

The specific calculations you can do depend on the level of measurement of your variables.

Using inferential statistics , you can:

• Make estimates about the population based on your sample data.
• Test hypotheses about a relationship between variables.

Regression and correlation tests look for associations between two or more variables, while comparison tests (such as t tests and ANOVAs ) look for differences in the outcomes of different groups.

Your choice of statistical test depends on various aspects of your research design, including the types of variables you’re dealing with and the distribution of your data.

Qualitative data analysis

In qualitative research, your data will usually be very dense with information and ideas. Instead of summing it up in numbers, you’ll need to comb through the data in detail, interpret its meanings, identify patterns, and extract the parts that are most relevant to your research question.

Two of the most common approaches to doing this are thematic analysis and discourse analysis .

There are many other ways of analysing qualitative data depending on the aims of your research. To get a sense of potential approaches, try reading some qualitative research papers in your field.

A sample is a subset of individuals from a larger population. Sampling means selecting the group that you will actually collect data from in your research.

For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

Statistical sampling allows you to test a hypothesis about the characteristics of a population. There are various sampling methods you can use to ensure that your sample is representative of the population as a whole.

Operationalisation means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioural avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalise the variables that you want to measure.

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts, and meanings, use qualitative methods .
• If you want to analyse a large amount of readily available data, use secondary data. If you want data specific to your purposes with control over how they are generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the ‘Cite this Scribbr article’ button to automatically add the citation to our free Reference Generator.

McCombes, S. (2023, March 20). Research Design | Step-by-Step Guide with Examples. Scribbr. Retrieved 29 April 2024, from https://www.scribbr.co.uk/research-methods/research-design/

Is this article helpful?

Shona McCombes

• Search Menu
• Advance articles
• AHFS First Release
• AJHP Voices
• AJHP Residents Edition
• Top Twenty-Five Articles
• ASHP National Surveys of Pharmacy Practice in Hospital Settings
• Medication Safety
• Pharmacy Technicians
• Specialty Pharmacy
• Emergency Preparedness and Clinician Well-being
• Author Guidelines
• Submission Site
• Open Access
• Information for Reviewers
• Self-Archiving Policy
• Author Instructions for Residents Edition
• Advertising and Corporate Services
• Advertising
• Reprints and ePrints
• Sponsored Supplements
• Editorial Board
• Permissions
• Journals on Oxford Academic
• Books on Oxford Academic
• < Previous

Developing and executing an effective research plan

• Article contents
• Figures & tables
• Supplementary Data

Robert J. Weber, Daniel J. Cobaugh, Developing and executing an effective research plan, American Journal of Health-System Pharmacy , Volume 65, Issue 21, 1 November 2008, Pages 2058–2065, https://doi.org/10.2146/ajhp070197

• Permissions Icon Permissions

Purpose. Practical approaches to successful implementation of practice-based research are examined.

Summary. In order to successfully complete a research project, its scope must be clearly defined. The research question and the specific aims or objectives should guide the study. For practice-based research, the clinical setting is the most likely source to find important research questions. The research idea should be realistic and relevant to the interests of the investigators and the organization and its patients. Once the lead investigator has developed a research idea, a comprehensive literature review should be performed. The aims of the project should be new, relevant, concise, and feasible. The researchers must budget adequate time to carefully consider, develop, and seek input on the research question and objectives using the principles of project management. Identifying a group of individuals that can work together to ensure successful completion of the proposed research should be one of the first steps in developing the research plan. Dividing work tasks can alleviate workload for individual members of the research team. The development of a timeline to help guide the execution of the research project plan is critical. Steps that can be especially time-consuming include obtaining financial support, garnering support from key stakeholders, and getting institutional review board consent. One of the primary goals of conducting research is to share the knowledge that has been gained through presentations at national and international conferences and publications in peer-reviewed biomedical journals.

Conclusion. Practice-based research presents numerous challenges, especially for new investigators. Integration of the principles of project management into research planning can lead to more efficient study execution and higher-quality results.

Email alerts

Citing articles via.

• Recommend to Your Librarian

Affiliations

• Online ISSN 1535-2900
• Print ISSN 1079-2082
• Copyright © 2024 American Society of Health-System Pharmacists
• About Oxford Academic
• Publish journals with us
• University press partners
• What we publish
• New features  
• Open access
• Institutional account management
• Rights and permissions
• Get help with access
• Accessibility
• Media enquiries
• Oxford University Press
• Oxford Languages
• University of Oxford

Oxford University Press is a department of the University of Oxford. It furthers the University's objective of excellence in research, scholarship, and education by publishing worldwide

• Copyright © 2024 Oxford University Press
• Cookie settings
• Cookie policy
• Privacy policy
• Legal notice

This Feature Is Available To Subscribers Only

Sign In or Create an Account

This PDF is available to Subscribers Only

For full access to this pdf, sign in to an existing account, or purchase an annual subscription.

Want to create or adapt books like this? Learn more about how Pressbooks supports open publishing practices.

14.1 What is experimental design and when should you use it?

Learning objectives.

Learners will be able to…

• Describe the purpose of experimental design research
• Describe nomethetic causality and the logic of experimental design
• Identify the characteristics of a basic experiment
• Discuss the relationship between dependent and independent variables in experiments
• Identify the three major types of experimental designs

Pre-awareness check (Knowledge)

What are your thoughts on the phrase ‘experiment’ in the realm of social sciences? In an experiment, what is the independent variable?

The basics of experiments

In social work research, experimental design is used to test the effects of treatments, interventions, programs, or other conditions to which individuals, groups, organizations, or communities may be exposed to. There are a lot of experiments social work researchers can use to explore topics such as treatments for depression, impacts of school-based mental health on student outcomes, or prevention of abuse of people with disabilities. The American Psychological Association defines an experiment   as:

a series of observations conducted under controlled conditions to study a relationship with the purpose of drawing causal inferences about that relationship. An experiment involves the manipulation of an independent variable , the measurement of a dependent variable , and the exposure of various participants to one or more of the conditions being studied. Random selection of participants and their random assignment to conditions also are necessary in experiments .

In experimental design, the independent variable is the intervention, treatment, or condition that is being investigated as a potential cause of change (i.e., the experimental condition ). The effect, or outcome, of the experimental condition is the dependent variable. Trying out a new restaurant, dating a new person – we often call these things “experiments.” However, a true social science experiment would include recruitment of a large enough sample, random assignment to control and experimental groups, exposing those in the experimental group to an experimental condition, and collecting observations at the end of the experiment.

Social scientists use this level of rigor and control to maximize the internal validity of their research. Internal validity is the confidence researchers have about whether the independent variable (e.g, treatment) truly produces a change in the dependent, or outcome, variable. The logic and features of experimental design are intended to help establish causality and to reduce threats to internal validity , which we will discuss in Section 14.5 .

Experiments attempt to establish a nomothetic causal relationship between two variables—the treatment and its intended outcome.  We discussed the four criteria for establishing nomothetic causality in Section 4.3 :

• plausibility,
• covariation,
• temporality, and
• nonspuriousness.

Experiments should establish plausibility , having a plausible reason why their intervention would cause changes in the dependent variable. Usually, a theory framework or previous empirical evidence will indicate the plausibility of a causal relationship.

Covariation can be established for causal explanations by showing that the “cause” and the “effect” change together.  In experiments, the cause is an intervention, treatment, or other experimental condition. Whether or not a research participant is exposed to the experimental condition is the independent variable. The effect in an experiment is the outcome being assessed and is the dependent variable in the study. When the independent and dependent variables covary, they can have a positive association (e.g., those exposed to the intervention have increased self-esteem) or a negative association (e.g., those exposed to the intervention have reduced anxiety).

Since researcher controls when the intervention is administered, they can be assured that changes in the independent variable (the treatment) happens before changes in the dependent variable (the outcome). In this way, experiments assure temporality .

Finally, one of the most important features of experiments is that they allow researchers to eliminate spurious variables to support the criterion of nonspuriousness . True experiments are usually conducted under strictly controlled conditions. The intervention is given in the same way to each person, with a minimal number of other variables that might cause their post-test scores to change.

The logic of experimental design

How do we know that one phenomenon causes another? The complexity of the social world in which we practice and conduct research means that causes of social problems are rarely cut and dry. Uncovering explanations for social problems is key to helping clients address them, and experimental research designs are one road to finding answers.

Just because two phenomena are related in some way doesn’t mean that one causes the other. Ice cream sales increase in the summer, and so does the rate of violent crime; does that mean that eating ice cream is going to make me violent? Obviously not, because ice cream is great. The reality of that association is far more complex—it could be that hot weather makes people more irritable and, at times, violent, while also making people want ice cream. More likely, though, there are other social factors not accounted for in the way we just described this association.

As we have discussed, experimental designs can help clear up at least some of this fog by allowing researchers to isolate the effect of interventions on dependent variables by controlling extraneous variables . In true experimental design (discussed in the next section) and quasi-experimental design, researchers accomplish this w ith a control group or comparison group and the experimental group . The experimental group is sometimes called the treatment group because people in the experimental group receive the treatment or are exposed to the experimental condition (but we will call it the experimental group in this chapter.) The control/comparison group does not receive the treatment or intervention. Instead they may receive what is known as “treatment as usual” or perhaps no treatment at all.

In a well-designed experiment, the control group should look almost identical to the experimental group in terms of demographics and other relevant factors. What if we want to know the effect of CBT on social anxiety, but we have learned in prior research that men tend to have a more difficult time overcoming social anxiety? We would want our control and experimental groups to have a similar portions of men, since ostensibly, both groups’ results would be affected by the men in the group. If your control group has 5 women, 6 men, and 4 non-binary people, then your experimental group should be made up of roughly the same gender balance to help control for the influence of gender on the outcome of your intervention. (In reality, the groups should be similar along other dimensions, as well, and your group will likely be much larger.) The researcher will use the same outcome measures for both groups and compare them, and assuming the experiment was designed correctly, get a pretty good answer about whether the intervention had an effect on social anxiety.

Random assignment [/pb_glossary], also called randomization, entails using a random process to decide which participants are put into the control or experimental group (which participants receive an intervention and which do not). By randomly assigning participants to a group, you can reduce the effect of extraneous variables on your research because there won’t be a systematic difference between the groups.

Do not confuse random assignment with random sampling . Random sampling is a method for selecting a sample from a population and is rarely used in psychological research. Random assignment is a method for assigning participants in a sample to the different conditions, and it is an important element of all experimental research in psychology and other related fields. Random sampling helps a great deal with external validity, or generalizability , whereas random assignment increases internal validity .

Other Features of Experiments that Help Establish Causality

To control for spuriousness (as well as meeting the three other criteria for establishing causality), experiments try to control as many aspects of the research process as possible: using control groups, having large enough sample sizes, standardizing the treatment, etc. Researchers in large experiments often employ clinicians or other research staff to help them. Researchers train their staff members exhaustively, provide pre-scripted responses to common questions, and control the physical environment of the experiment so each person who participates receives the exact same treatment. Experimental researchers also document their procedures, so that others can review them and make changes in future research if they think it will improve on the ability to control for spurious variables.

An interesting example is Bruce Alexander’s (2010) Rat Park experiments. Much of the early research conducted on addictive drugs, like heroin and cocaine, was conducted on animals other than humans, usually mice or rats. The scientific consensus up until Alexander’s experiments was that cocaine and heroin were so addictive that rats, if offered the drugs, would consume them repeatedly until they perished. Researchers claimed this behavior explained how addiction worked in humans, but Alexander was not so sure. He knew rats were social animals and the experimental procedure from previous experiments did not allow them to socialize. Instead, rats were kept isolated in small cages with only food, water, and metal walls. To Alexander, social isolation was a spurious variable, causing changes in addictive behavior not due to the drug itself. Alexander created an experiment of his own, in which rats were allowed to run freely in an interesting environment, socialize and mate with other rats, and of course, drink from a solution that contained an addictive drug. In this environment, rats did not become hopelessly addicted to drugs. In fact, they had little interest in the substance. To Alexander, the results of his experiment demonstrated that social isolation was more of a causal factor for addiction than the drug itself.

One challenge with Alexander’s findings is that subsequent researchers have had mixed success replicating his findings (e.g., Petrie, 1996; Solinas, Thiriet, El Rawas, Lardeux, & Jaber, 2009). Replication involves conducting another researcher’s experiment in the same manner and seeing if it produces the same results. If the causal relationship is real, it should occur in all (or at least most) rigorous replications of the experiment.

Replicability

[INSERT A PARAGRAPH ABOUT REPLICATION/REPRODUCTION HERE. CAN USE/REFERENCE THIS   IF IT’S HELPFUL; include glossary definition as well as other general info]

To allow for easier replication, researchers should describe their experimental methods diligently. Researchers with the Open Science Collaboration (2015) [1] conducted the Reproducibility Project , which caused a significant controversy regarding the validity of psychological studies. The researchers with the project attempted to reproduce the results of 100 experiments published in major psychology journals since 2008. What they found was shocking. Although 97% of the original studies reported significant results, only 36% of the replicated studies had significant findings. The average effect size in the replication studies was half that of the original studies. The implications of the Reproducibility Project are potentially staggering, and encourage social scientists to carefully consider the validity of their reported findings and that the scientific community take steps to ensure researchers do not cherry-pick data or change their hypotheses simply to get published.

Generalizability

Let’s return to Alexander’s Rat Park study and consider the implications of his experiment for substance use professionals.  The conclusions he drew from his experiments on rats were meant to be generalized to the population. If this could be done, the experiment would have a high degree of external validity , which is the degree to which conclusions generalize to larger populations and different situations. Alexander argues his conclusions about addiction and social isolation help us understand why people living in deprived, isolated environments may become addicted to drugs more often than those in more enriching environments. Similarly, earlier rat researchers argued their results showed these drugs were instantly addictive to humans, often to the point of death.

Neither study’s results will match up perfectly with real life. There are clients in social work practice who may fit into Alexander’s social isolation model, but social isolation is complex. Clients can live in environments with other sociable humans, work jobs, and have romantic relationships; does this mean they are not socially isolated? On the other hand, clients may face structural racism, poverty, trauma, and other challenges that may contribute to their social environment. Alexander’s work helps understand clients’ experiences, but the explanation is incomplete. Human existence is more complicated than the experimental conditions in Rat Park.

Effectiveness versus Efficacy

Social workers are especially attentive to how social context shapes social life. This consideration points out a potential weakness of experiments. They can be rather artificial. When an experiment demonstrates causality under ideal, controlled circumstances, it establishes the efficacy of an intervention.

How often do real-world social interactions occur in the same way that they do in a controlled experiment? Experiments that are conducted in community settings by community practitioners are less easily controlled than those conducted in a lab or with researchers who adhere strictly to research protocols delivering the intervention. When an experiment demonstrates causality in a real-world setting that is not tightly controlled, it establishes the effectiveness of the intervention.

The distinction between efficacy and effectiveness demonstrates the tension between internal and external validity. Internal validity and external validity are conceptually linked. Internal validity refers to the degree to which the intervention causes its intended outcomes, and external validity refers to how well that relationship applies to different groups and circumstances than the experiment. However, the more researchers tightly control the environment to ensure internal validity, the more they may risk external validity for generalizing their results to different populations and circumstances. Correspondingly, researchers whose settings are just like the real world will be less able to ensure internal validity, as there are many factors that could pollute the research process. This is not to suggest that experimental research findings cannot have high levels of both internal and external validity, but that experimental researchers must always be aware of this potential weakness and clearly report limitations in their research reports.

Types of Experimental Designs

Experimental design is an umbrella term for a research method that is designed to test hypotheses related to causality under controlled conditions. Table 14.1 describes the three major types of experimental design (pre-experimental, quasi-experimental, and true experimental) and presents subtypes for each. As we will see in the coming sections, some types of experimental design are better at establishing causality than others. It’s also worth considering that true experiments, which most effectively establish causality , are often difficult and expensive to implement. Although the other experimental designs aren’t perfect, they still produce useful, valid evidence and may be more feasible to carry out.

Key Takeaways

• Experimental designs are useful for establishing causality, but some types of experimental design do this better than others.
• Experiments help researchers isolate the effect of the independent variable on the dependent variable by controlling for the effect of extraneous variables .
• Experiments use a control/comparison group and an experimental group to test the effects of interventions. These groups should be as similar to each other as possible in terms of demographics and other relevant factors.
• True experiments have control groups with randomly assigned participants; quasi-experimental types of experiments have comparison groups to which participants are not randomly assigned; pre-experimental designs do not have a comparison group.

TRACK 1 (IF YOU  ARE  CREATING A RESEARCH PROPOSAL FOR THIS CLASS):

• Think about the research project you’ve been designing so far. How might you use a basic experiment to answer your question? If your question isn’t explanatory, try to formulate a new explanatory question and consider the usefulness of an experiment.
• Why is establishing a simple relationship between two variables not indicative of one causing the other?

TRACK 2 (IF YOU  AREN’T  CREATING A RESEARCH PROPOSAL FOR THIS CLASS):

Imagine you are interested in studying child welfare practice. You are interested in learning more about community-based programs aimed to prevent child maltreatment and to prevent out-of-home placement for children.

• Think about the research project stated above. How might you use a basic experiment to look more into this research topic? Try to formulate an explanatory question and consider the usefulness of an experiment.
• Open Science Collaboration. (2015). Estimating the reproducibility of psychological science. Science, 349 (6251), aac4716. Doi: 10.1126/science.aac4716 ↵

an operation or procedure carried out under controlled conditions in order to discover an unknown effect or law, to test or establish a hypothesis, or to illustrate a known law.

treatment, intervention, or experience that is being tested in an experiment (the independent variable) that is received by the experimental group and not by the control group.

Ability to say that one variable "causes" something to happen to another variable. Very important to assess when thinking about studies that examine causation such as experimental or quasi-experimental designs.

circumstances or events that may affect the outcome of an experiment, resulting in changes in the research participants that are not a result of the intervention, treatment, or experimental condition being tested

causal explanations that can be universally applied to groups, such as scientific laws or universal truths

as a criteria for causal relationship, the relationship must make logical sense and seem possible

when the values of two variables change at the same time

as a criteria for causal relationship, the cause must come before the effect

an association between two variables that is NOT caused by a third variable

variables and characteristics that have an effect on your outcome, but aren't the primary variable whose influence you're interested in testing.

the group of participants in our study who do not receive the intervention we are researching in experiments with random assignment

the group of participants in our study who do not receive the intervention we are researching in experiments without random assignment

in experimental design, the group of participants in our study who do receive the intervention we are researching

The ability to apply research findings beyond the study sample to some broader population,

This is a synonymous term for generalizability - the ability to apply the findings of a study beyond the sample to a broader population.

performance of an intervention under ideal and controlled circumstances, such as in a lab or delivered by trained researcher-interventionists

The performance of an intervention under "real-world" conditions that are not closely controlled and ideal

the idea that one event, behavior, or belief will result in the occurrence of another, subsequent event, behavior, or belief

Doctoral Research Methods in Social Work Copyright © by Mavs Open Press. All Rights Reserved.

Share This Book

Developing and executing an effective research plan

Affiliation.

• 1 University of Pittsburgh Medical Center and Department of Pharmacy and Therapeutics, University of Pittsburgh School of Pharmacy, Pittsburgh, PA, USA.
• PMID: 18945867
• DOI: 10.2146/ajhp070197

Purpose: Practical approaches to successful implementation of practice-based research are examined.

Summary: In order to successfully complete a research project, its scope must be clearly defined. The research question and the specific aims or objectives should guide the study. For practice-based research, the clinical setting is the most likely source to find important research questions. The research idea should be realistic and relevant to the interests of the investigators and the organization and its patients. Once the lead investigator has developed a research idea, a comprehensive literature review should be performed. The aims of the project should be new, relevant, concise, and feasible. The researchers must budget adequate time to carefully consider, develop, and seek input on the research question and objectives using the principles of project management. Identifying a group of individuals that can work together to ensure successful completion of the proposed research should be one of the first steps in developing the research plan. Dividing work tasks can alleviate workload for individual members of the research team. The development of a timeline to help guide the execution of the research project plan is critical. Steps that can be especially time-consuming include obtaining financial support, garnering support from key stakeholders, and getting institutional review board consent. One of the primary goals of conducting research is to share the knowledge that has been gained through presentations at national and international conferences and publications in peer-reviewed biomedical journals.

Conclusion: Practice-based research presents numerous challenges, especially for new investigators. Integration of the principles of project management into research planning can lead to more efficient study execution and higher-quality results.

Publication types

• Biomedical Research / methods*
• Biomedical Research / trends*
• Pharmacists / trends
• Professional Practice / trends
• Research Design / trends*

Psychological Research

Experimental research.

As you’ve learned, the only way to establish that there is a cause-and-effect relationship between two variables is to conduct a scientific experiment. Experiment has a different meaning in the scientific context than in everyday life. In everyday conversation, we often use it to describe trying something for the first time, such as experimenting with a new hairstyle or new food. However, in the scientific context, an experiment has precise requirements for design and implementation.

Video 1.  Experimental Research Design  provides explanation and examples for correlational research. A closed-captioned version of this video is available here .

The Experimental Hypothesis

In order to conduct an experiment, a researcher must have a specific hypothesis to be tested. As you’ve learned, hypotheses can be formulated either through direct observation of the real world or after careful review of previous research. For example, if you think that children should not be allowed to watch violent programming on television because doing so would cause them to behave more violently, then you have basically formulated a hypothesis—namely, that watching violent television programs causes children to behave more violently. How might you have arrived at this particular hypothesis? You may have younger relatives who watch cartoons featuring characters using martial arts to save the world from evildoers, with an impressive array of punching, kicking, and defensive postures. You notice that after watching these programs for a while, your young relatives mimic the fighting behavior of the characters portrayed in the cartoon. Seeing behavior like this right after a child watches violent television programming might lead you to hypothesize that viewing violent television programming leads to an increase in the display of violent behaviors. These sorts of personal observations are what often lead us to formulate a specific hypothesis, but we cannot use limited personal observations and anecdotal evidence to test our hypothesis rigorously. Instead, to find out if real-world data supports our hypothesis, we have to conduct an experiment.

Designing an Experiment

The most basic experimental design involves two groups: the experimental group and the control group. The two groups are designed to be the same except for one difference— experimental manipulation. The  experimental group  gets the experimental manipulation—that is, the treatment or variable being tested (in this case, violent TV images)—and the  control group  does not. Since experimental manipulation is the only difference between the experimental and control groups, we can be sure that any differences between the two are due to experimental manipulation rather than chance.

In our example of how violent television programming might affect violent behavior in children, we have the experimental group view violent television programming for a specified time and then measure their violent behavior. We measure the violent behavior in our control group after they watch nonviolent television programming for the same amount of time. It is important for the control group to be treated similarly to the experimental group, with the exception that the control group does not receive the experimental manipulation. Therefore, we have the control group watch non-violent television programming for the same amount of time as the experimental group.

We also need to define precisely, or operationalize, what is considered violent and nonviolent. An  operational definition  is a description of how we will measure our variables, and it is important in allowing others to understand exactly how and what a researcher measures in a particular experiment. In operationalizing violent behavior, we might choose to count only physical acts like kicking or punching as instances of this behavior, or we also may choose to include angry verbal exchanges. Whatever we determine, it is important that we operationalize violent behavior in such a way that anyone who hears about our study for the first time knows exactly what we mean by violence. This aids peoples’ ability to interpret our data as well as their capacity to repeat our experiment should they choose to do so.

Once we have operationalized what is considered violent television programming and what is considered violent behavior from our experiment participants, we need to establish how we will run our experiment. In this case, we might have participants watch a 30-minute television program (either violent or nonviolent, depending on their group membership) before sending them out to a playground for an hour where their behavior is observed and the number and type of violent acts are recorded.

Ideally, the people who observe and record the children’s behavior are unaware of who was assigned to the experimental or control group, in order to control for experimenter bias.  Experimenter bias  refers to the possibility that a researcher’s expectations might skew the results of the study. Remember, conducting an experiment requires a lot of planning, and the people involved in the research project have a vested interest in supporting their hypotheses. If the observers knew which child was in which group, it might influence how much attention they paid to each child’s behavior as well as how they interpreted that behavior. By being blind to which child is in which group, we protect against those biases. This situation is a  single-blind study , meaning that the participants are unaware as to which group they are in (experiment or control group) while the researcher knows which participants are in each group.

In a  double-blind study , both the researchers and the participants are blind to group assignments. Why would a researcher want to run a study where no one knows who is in which group? Because by doing so, we can control for both experimenter and participant expectations. If you are familiar with the phrase  placebo effect , you already have some idea as to why this is an important consideration. The placebo effect occurs when people’s expectations or beliefs influence or determine their experience in a given situation. In other words, simply expecting something to happen can actually make it happen.

Why is that? Imagine that you are a participant in this study, and you have just taken a pill that you think will improve your mood. Because you expect the pill to have an effect, you might feel better simply because you took the pill and not because of any drug actually contained in the pill—this is the placebo effect.

To make sure that any effects on mood are due to the drug and not due to expectations, the control group receives a placebo (in this case, a sugar pill). Now everyone gets a pill, and once again, neither the researcher nor the experimental participants know who got the drug and who got the sugar pill. Any differences in mood between the experimental and control groups can now be attributed to the drug itself rather than to experimenter bias or participant expectations.

Video 2.  Introduction to Experimental Design introduces fundamental elements for experimental research design.

Independent and Dependent Variables

In a research experiment, we strive to study whether changes in one thing cause changes in another. To achieve this, we must pay attention to two important variables, or things that can be changed, in any experimental study: the independent variable and the dependent variable. An  independent variable  is manipulated or controlled by the experimenter. In a well-designed experimental study, the independent variable is the only important difference between the experimental and control groups. In our example of how violent television programs affect children’s display of violent behavior, the independent variable is the type of program—violent or nonviolent—viewed by participants in the study (Figure 2.3). A  dependent variable  is what the researcher measures to see how much effect the independent variable had. In our example, the dependent variable is the number of violent acts displayed by the experimental participants.

Figure  1.  In an experiment, manipulations of the independent variable are expected to result in changes in the dependent variable.

We expect that the dependent variable will change as a function of the independent variable. In other words, the dependent variable  depends  on the independent variable. A good way to think about the relationship between the independent and dependent variables is with this question: What effect does the independent variable have on the dependent variable? Returning to our example, what effect does watching a half-hour of violent television programming or nonviolent television programming have on the number of incidents of physical aggression displayed on the playground?

Selecting and Assigning Experimental Participants

Now that our study is designed, we need to obtain a sample of individuals to include in our experiment. Our study involves human participants, so we need to determine who to include.  Participants  are the subjects of psychological research, and as the name implies, individuals who are involved in psychological research actively participate in the process. Often, psychological research projects rely on college students to serve as participants. In fact, the vast majority of research in psychology subfields has historically involved students as research participants (Sears, 1986; Arnett, 2008). But are college students truly representative of the general population? College students tend to be younger, more educated, more liberal, and less diverse than the general population. Although using students as test subjects is an accepted practice, relying on such a limited pool of research participants can be problematic because it is difficult to generalize findings to the larger population.

Our hypothetical experiment involves children, and we must first generate a sample of child participants. Samples are used because populations are usually too large to reasonably involve every member in our particular experiment (Figure 2.4). If possible, we should use a random sample (there are other types of samples, but for the purposes of this chapter, we will focus on random samples). A  random sample  is a subset of a larger population in which every member of the population has an equal chance of being selected. Random samples are preferred because if the sample is large enough we can be reasonably sure that the participating individuals are representative of the larger population. This means that the percentages of characteristics in the sample—sex, ethnicity, socioeconomic level, and any other characteristics that might affect the results—are close to those percentages in the larger population.

In our example, let’s say we decide our population of interest is fourth graders. But all fourth graders is a very large population, so we need to be more specific; instead, we might say our population of interest is all fourth graders in a particular city. We should include students from various income brackets, family situations, races, ethnicities, religions, and geographic areas of town. With this more manageable population, we can work with the local schools in selecting a random sample of around 200 fourth-graders that we want to participate in our experiment.

In summary, because we cannot test all of the fourth graders in a city, we want to find a group of about 200 that reflects the composition of that city. With a representative group, we can generalize our findings to the larger population without fear of our sample being biased in some way.

Figure  2.  Researchers may work with (a) a large population or (b) a sample group that is a subset of the larger population.

Now that we have a sample, the next step of the experimental process is to split the participants into experimental and control groups through random assignment. With  random assignment , all participants have an equal chance of being assigned to either group. There is statistical software that will randomly assign each of the fourth graders in the sample to either the experimental or the control group.

Random assignment is critical for sound experimental design. With sufficiently large samples, random assignment makes it unlikely that there are systematic differences between the groups. So, for instance, it would be improbable that we would get one group composed entirely of males, a given ethnic identity, or a given religious ideology. This is important because if the groups were systematically different before the experiment began, we would not know the origin of any differences we find between the groups: Were the differences preexisting, or were they caused by manipulation of the independent variable? Random assignment allows us to assume that any differences observed between experimental and control groups result from the manipulation of the independent variable.

Use this  online tool to generate randomized numbers instantly and to learn more about random sampling and assignments.

Issues to Consider

While experiments allow scientists to make cause-and-effect claims, they are not without problems. True experiments require the experimenter to manipulate an independent variable, and that can complicate many questions that psychologists might want to address. For instance, imagine that you want to know what effect sex (the independent variable) has on spatial memory (the dependent variable). Although you can certainly look for differences between males and females on a task that taps into spatial memory, you cannot directly control a person’s sex. We categorize this type of research approach as quasi-experimental and recognize that we cannot make cause-and-effect claims in these circumstances.

Experimenters are also limited by ethical constraints. For instance, you would not be able to conduct an experiment designed to determine if experiencing abuse as a child leads to lower levels of self-esteem among adults. To conduct such an experiment, you would need to randomly assign some experimental participants to a group that receives abuse, and that experiment would be unethical.

Interpreting Experimental Findings

Once data is collected from both the experimental and the control groups, a  statistical analysis  is conducted to find out if there are meaningful differences between the two groups. The statistical analysis determines how likely any difference found is due to chance (and thus not meaningful). In psychology, group differences are considered meaningful, or significant, if the odds that these differences occurred by chance alone are 5 percent or less. Stated another way, if we repeated this experiment 100 times, we would expect to find the same results at least 95 times out of 100.

The greatest strength of experiments is the ability to assert that any significant differences in the findings are caused by the independent variable. This occurs because random selection, random assignment, and a design that limits the effects of both experimenter bias and participant expectancy should create groups that are similar in composition and treatment. Therefore, any difference between the groups is attributable to the independent variable, and now we can finally make a causal statement. If we find that watching a violent television program results in more violent behavior than watching a nonviolent program, we can safely say that watching violent television programs causes an increase in the display of violent behavior.

Writing a Research Proposal

• First Online: 10 April 2022

Cite this chapter

• Fahimeh Tabatabaei 3 &
• Lobat Tayebi 3  

839 Accesses

A research proposal is a roadmap that brings the researcher closer to the objectives, takes the research topic from a purely subjective mind, and manifests an objective plan. It shows us what steps we need to take to reach the objective, what questions we should answer, and how much time we need. It is a framework based on which you can perform your research in a well-organized and timely manner. In other words, by writing a research proposal, you get a map that shows the direction to the destination (answering the research question). If the proposal is poorly prepared, after spending a lot of energy and money, you may realize that the result of the research has nothing to do with the initial objective, and the study may end up nowhere. Therefore, writing the proposal shows that the researcher is aware of the proper research and can justify the significance of his/her idea.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

• Available as PDF
• Read on any device
• Instant download
• Own it forever
• Available as EPUB and PDF
• Compact, lightweight edition
• Dispatched in 3 to 5 business days
• Free shipping worldwide - see info
• Durable hardcover edition

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

A. Gholipour, E.Y. Lee, S.K. Warfield, The anatomy and art of writing a successful grant application: A practical step-by-step approach. Pediatr. Radiol. 44 (12), 1512–1517 (2014)

Article   Google Scholar  

L.S. Marshall, Research commentary: Grant writing: Part I first things first …. J. Radiol. Nurs. 31 (4), 154–155 (2012)

E.K. Proctor, B.J. Powell, A.A. Baumann, A.M. Hamilton, R.L. Santens, Writing implementation research grant proposals: Ten key ingredients. Implement. Sci. 7 (1), 96 (2012)

K.C. Chung, M.J. Shauver, Fundamental principles of writing a successful grant proposal. J. Hand Surg. Am. 33 (4), 566–572 (2008)

A.A. Monte, A.M. Libby, Introduction to the specific aims page of a grant proposal. Kline JA, editor. Acad. Emerg. Med. 25 (9), 1042–1047 (2018)

P. Kan, M.R. Levitt, W.J. Mack, R.M. Starke, K.N. Sheth, F.C. Albuquerque, et al., National Institutes of Health grant opportunities for the neurointerventionalist: Preparation and choosing the right mechanism. J. Neurointerv. Surg. 13 (3), 287–289 (2021)

A.M. Goldstein, S. Balaji, A.A. Ghaferi, A. Gosain, M. Maggard-Gibbons, B. Zuckerbraun, et al., An algorithmic approach to an impactful specific aims page. Surgery 169 (4), 816–820 (2021)

S. Engberg, D.Z. Bliss, Writing a grant proposal—Part 1. J. Wound Ostomy Cont. Nurs. 32 (3), 157–162 (2005)

D.Z. Bliss, K. Savik, Writing a grant proposal—Part 2. J. Wound Ostomy Cont. Nurs. 32 (4), 226–229 (2005)

D.Z. Bliss, Writing a grant proposal—Part 6. J. Wound Ostomy Cont. Nurs. 32 (6), 365–367 (2005)

J.C. Liu, M.A. Pynnonen, M. St John, E.L. Rosenthal, M.E. Couch, C.E. Schmalbach, Grant-writing pearls and pitfalls. Otolaryngol. Neck. Surg. 154 (2), 226–232 (2016)

R.J. Santen, E.J. Barrett, H.M. Siragy, L.S. Farhi, L. Fishbein, R.M. Carey, The jewel in the crown: Specific aims section of investigator-initiated grant proposals. J. Endocr. Soc. 1 (9), 1194–1202 (2017)

O.J. Arthurs, Think it through first: Questions to consider in writing a successful grant application. Pediatr. Radiol. 44 (12), 1507–1511 (2014)

M. Monavarian, Basics of scientific and technical writing. MRS Bull. 46 (3), 284–286 (2021)

Additional Resources

https://grants.nih.gov

https://grants.nih.gov/grants/oer.htm

https://www.ninr.nih.gov

https://www.niaid.nih.gov

http://www.grantcentral.com

http://www.saem.org/research

http://www.cfda.gov

http://www.ahrq.gov

http://www.nsf/gov

Download references

Author information

Authors and affiliations.

School of Dentistry, Marquette University, Milwaukee, WI, USA

Fahimeh Tabatabaei & Lobat Tayebi

You can also search for this author in PubMed   Google Scholar

Rights and permissions

Reprints and permissions

Copyright information

© 2022 Springer Nature Switzerland AG

About this chapter

Tabatabaei, F., Tayebi, L. (2022). Writing a Research Proposal. In: Research Methods in Dentistry. Springer, Cham. https://doi.org/10.1007/978-3-030-98028-3_4

Download citation

DOI : https://doi.org/10.1007/978-3-030-98028-3_4

Published : 10 April 2022

Publisher Name : Springer, Cham

Print ISBN : 978-3-030-98027-6

Online ISBN : 978-3-030-98028-3

eBook Packages : Engineering Engineering (R0)

Share this chapter

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Publish with us

Policies and ethics

• Find a journal
• Track your research

Want to create or adapt books like this? Learn more about how Pressbooks supports open publishing practices.

Chapter 2: Psychological Research

Experimental Research

As you’ve learned, the only way to establish that there is a cause-and-effect relationship between two variables is to conduct a scientific experiment. Experiment has a different meaning in the scientific context than in everyday life. In everyday conversation, we often use it to describe trying something for the first time, such as experimenting with a new hairstyle or new food. However, in the scientific context, an experiment has precise requirements for design and implementation.

Video 2.10 Experimental Research Design  provides explanations and examples for correlational research.

The Experimental Hypothesis

In order to conduct an experiment, a researcher must have a specific hypothesis to be tested. As you’ve learned, hypotheses can be formulated either through direct observation of the real world or after careful review of previous research. For example, if you think that children should not be allowed to watch violent programming on television because doing so would cause them to behave more violently, then you have basically formulated a hypothesis—namely, that watching violent television programs causes children to behave more violently. How might you have arrived at this particular hypothesis? You may have younger relatives who watch cartoons featuring characters using martial arts to save the world from evildoers, with an impressive array of punching, kicking, and defensive postures. You notice that after watching these programs for a while, your young relatives mimic the fighting behavior of the characters portrayed in the cartoon. Seeing behavior like this right after a child watches violent television programming might lead you to hypothesize that viewing violent television programming leads to an increase in the display of violent behaviors. These sorts of personal observations are what often lead us to formulate a specific hypothesis, but we cannot use limited personal observations and anecdotal evidence to test our hypothesis rigorously. Instead, to find out if real-world data supports our hypothesis, we have to conduct an experiment.

Designing an Experiment

The most basic experimental design involves two groups: the experimental group and the control group. The two groups are designed to be the same except for one difference— experimental manipulation. The  experimental group  gets the experimental manipulation—that is, the treatment or variable being tested (in this case, violent TV images)—and the  control group does not. Because experimental manipulation is the only difference between the experimental and control groups, we can be sure that any differences between the two are due to experimental manipulation rather than chance.

In our example of how violent television programming might affect violent behavior in children, we have the experimental group view violent television programming for a specified time and then measure their violent behavior. We measure the violent behavior in our control group after they watch nonviolent television programming for the same amount of time. It is important for the control group to be treated similarly to the experimental group, with the exception that the control group does not receive the experimental manipulation. Therefore, we have the control group watch non-violent television programming for the same amount of time as the experimental group.

We also need to define precisely, or operationalize, what is considered violent and nonviolent. An  operational definition  is a description of how we will measure our variables, and it is important in allowing others to understand exactly how and what a researcher measures in a particular experiment. In operationalizing violent behavior, we might choose to count only physical acts like kicking or punching as instances of this behavior, or we also may choose to include angry verbal exchanges. Whatever we determine, it is important that we operationalize violent behavior in such a way that anyone who hears about our study for the first time knows exactly what we mean by violence. This aids peoples’ ability to interpret our data as well as their capacity to repeat our experiment should they choose to do so.

Once we have operationalized what is considered violent television programming and what is considered violent behavior from our experiment participants, we need to establish how we will run our experiment. In this case, we might have participants watch a 30-minute television program (either violent or nonviolent, depending on their group membership) before sending them out to a playground for an hour where their behavior is observed and the number and type of violent acts are recorded.

Ideally, the people who observe and record the children’s behavior are unaware of who was assigned to the experimental or control group, in order to control for experimenter bias.  Experimenter bias  refers to the possibility that a researcher’s expectations might skew the results of the study. Remember, conducting an experiment requires a lot of planning, and the people involved in the research project have a vested interest in supporting their hypotheses. If the observers knew which child was in which group, it might influence how much attention they paid to each child’s behavior as well as how they interpreted that behavior. By being blind to which child is in which group, we protect against those biases. This situation is a  single-blind study , meaning that the participants are unaware as to which group they are in (experiment or control group) while the researcher knows which participants are in each group.

In a  double-blind study , both the researchers and the participants are blind to group assignments. Why would a researcher want to run a study where no one knows who is in which group? Because by doing so, we can control for both experimenter and participant expectations. If you are familiar with the phrase  placebo effect , you already have some idea as to why this is an important consideration. The placebo effect occurs when people’s expectations or beliefs influence or determine their experience in a given situation. In other words, simply expecting something to happen can actually make it happen.

Why is that? Imagine that you are a participant in this study, and you have just taken a pill that you think will improve your mood. Because you expect the pill to have an effect, you might feel better simply because you took the pill and not because of any drug actually contained in the pill—this is the placebo effect.

To make sure that any effects on mood are due to the drug and not due to expectations, the control group receives a placebo (in this case, a sugar pill). Now everyone gets a pill, and once again, neither the researcher nor the experimental participants know who got the drug and who got the sugar pill. Any differences in mood between the experimental and control groups can now be attributed to the drug itself rather than to experimenter bias or participant expectations.

Video 2.11 Introduction to Experimental Design introduces fundamental elements for experimental research design.

Independent and Dependent Variables

In a research experiment, we strive to study whether changes in one thing cause changes in another. To achieve this, we must pay attention to two important variables, or things that can be changed, in any experimental study: the independent variable and the dependent variable. An independent variable is manipulated or controlled by the experimenter. In a well-designed experimental study, the independent variable is the only important difference between the experimental and control groups. In our example of how violent television programs affect children’s display of violent behavior, the independent variable is the type of program—violent or nonviolent—viewed by participants in the study (Figure 2.11). A dependent variable is what the researcher measures to see how much effect the independent variable had. In our example, the dependent variable is the number of violent acts displayed by the experimental participants.

Figure 2.1 1  In an experiment, manipulations of the independent variable are expected to result in changes in the dependent variable.

We expect that the dependent variable will change as a function of the independent variable. In other words, the dependent variable  depends  on the independent variable. A good way to think about the relationship between the independent and dependent variables is with this question: What effect does the independent variable have on the dependent variable? Returning to our example, what effect does watching a half-hour of violent television programming or nonviolent television programming have on the number of incidents of physical aggression displayed on the playground?

Selecting and Assigning Experimental Participants

Now that our study is designed, we need to obtain a sample of individuals to include in our experiment. Our study involves human participants, so we need to determine who to include.  Participants are the individuals of psychological research, and as the name implies, individuals who are involved in psychological research actively participate in the process. Often, psychological research projects rely on college students to serve as participants. In fact, the vast majority of research in psychology subfields has historically involved students as research participants (Arnett, 2008; Sears, 1986). But are college students truly representative of the general population? College students tend to be younger, more educated, more liberal, and less diverse than the general population. Although using students as test subjects is an accepted practice, relying on such a limited pool of research participants can be problematic because it is difficult to generalize findings to the larger population.

Our hypothetical experiment involves children, and we must first generate a sample of child participants. Samples are used because populations are usually too large to reasonably involve every member in our particular experiment. If possible, we should use a random sample (there are other types of samples, but for the purposes of this chapter, we will focus on random samples). A random sample is a subset of a larger population in which every member of the population has an equal chance of being selected. Random samples are preferred because if the sample is large enough we can be reasonably sure that the participating individuals are representative of the larger population. This means that the percentages of characteristics in the sample—sex, ethnicity, socioeconomic level, and any other characteristics that might affect the results—are close to those percentages in the larger population.

In our example, let’s say we decide our population of interest is fourth graders. But all fourth graders is a very large population, so we need to be more specific; instead, we might say our population of interest is all fourth graders in a particular city. We should include students from various income brackets, family situations, races, ethnicities, religions, and geographic areas of town. With this more manageable population, we can work with the local schools in selecting a random sample of around 200 fourth-graders that we want to participate in our experiment.

In summary, because we cannot test all of the fourth graders in a city, we want to find a group of about 200 that reflects the composition of that city. With a representative group, we can generalize our findings to the larger population without fear of our sample being biased in some way.

Figure  2.12  Researchers may work with (a) a large population or (b) a sample group that is a subset of the larger population.

Now that we have a sample, the next step of the experimental process is to split the participants into experimental and control groups through random assignment. With random assignment, all participants have an equal chance of being assigned to either group. There is statistical software that will randomly assign each of the fourth graders in the sample to either the experimental or the control group.

Random assignment is critical for sound experimental design. With sufficiently large samples, random assignment makes it unlikely that there are systematic differences between the groups. So, for instance, it would be improbable that we would get one group composed entirely of males, a given ethnic identity, or a given religious ideology. This is important because if the groups were systematically different before the experiment began, we would not know the origin of any differences we find between the groups: Were the differences preexisting, or were they caused by manipulation of the independent variable? Random assignment allows us to assume that any differences observed between experimental and control groups result from the manipulation of the independent variable.

Use this  online tool to generate randomized numbers instantly and to learn more about random sampling and assignments.

Issues to Consider

While experiments allow scientists to make cause-and-effect claims, they are not without problems. True experiments require the experimenter to manipulate an independent variable, and that can complicate many questions that psychologists might want to address. For instance, imagine that you want to know what effect sex (the independent variable) has on spatial memory (the dependent variable). Although you can certainly look for differences between males and females on a task that taps into spatial memory, you cannot control a person’s sex. We categorize this type of research approach as quasi-experimental and recognize that we cannot make cause-and-effect claims in these circumstances.

Experimenters are also limited by ethical constraints. For instance, you would not be able to conduct an experiment designed to determine if experiencing abuse as a child leads to lower levels of self-esteem among adults. To conduct such an experiment, you would need to randomly assign some experimental participants to a group that receives abuse, and that experiment would be unethical.

Interpreting Experimental Findings

Once data is collected from both the experimental and the control groups, a statistical analysis is conducted to find out if there are meaningful differences between the two groups. The statistical analysis determines how likely any difference found is due to chance (and thus not meaningful). In psychology, group differences are considered meaningful, or significant, if the odds that these differences occurred by chance alone are 5 percent or less. Stated another way, if we repeated this experiment 100 times, we would expect to find the same results at least 95 times out of 100.

The greatest strength of experiments is the ability to assert that any significant differences in the findings are caused by the independent variable. This occurs because random selection, random assignment, and a design that limits the effects of both experimenter bias and participant expectancy should create groups that are similar in composition and treatment. Therefore, any difference between the groups is attributable to the independent variable, and now we can finally make a causal statement. If we find that watching a violent television program results in more violent behavior than watching a nonviolent program, we can safely say that watching violent television programs causes an increase in the display of violent behavior.

the group of participants in an experiment who receive the independent variable

a comparison group that is equivalent to the experimental group, but is not given the independent variable

the possibility that a researcher’s expectations might skew the results of the study

the participants are unaware as to which group they are in (experiment or control group) while the researcher knows which participants are in each group

both the researchers and the participants are blind to group assignments

occurs when people's expectations or beliefs influence or determine their experience in a given situation

variable that is manipulated or controlled by the experimenter

what the researcher measures to see how much effect the independent variable had

individuals who are involved in psychological research actively participate in the process

every member of the population an equal chance of being selected for the sample

Child and Adolescent Development Copyright © 2023 by Krisztina Jakobsen and Paige Fischer is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

Share This Book